What does IOT have to do with Automobiles?

In countries like the United Kingdom, the cost of insurance is really high. Sometimes it rises so much that it becomes better for owners to scrap an old car and buy a new one. So what does this have to do with IoT? To understand that, we first need to see how insurance premiums are calculated in those countries.

How insurance premiums are calculated

Premiums are based on many parameters, such as vehicle value, how safe the vehicle is to drive, the age of the driver and the location where the vehicle will be parked.

For example, a rear-wheel drive, 400 bhp sports car driven by a youngster and parked in open parking by the side of the road will attract a heavy premium. Insurance companies argue that:

• Rear-wheel drive is inherently less safe than FWD or AWD.
• 400 bhp is too much and will lead to accidents.
• Youngsters are more prone to drive rashly and so have a higher chance of accidents.
• Parking by the side of the road makes it more likely that the car will be stolen or broken into.

But is this the right way to decide whether a driver is safe on the road? Insurers are stereotyping based on vehicle make and driver age. To change this, insurance companies came up with the concept of "Usage-Based Insurance".

Usage-Based Insurance

UBI works by plugging an OBD dongle into the customer's vehicle. The dongle is a micro-controller-based device that reads diagnostics data from the vehicle's ECU, such as vehicle speed, gear, engine rpm and throttle position. It also has a built-in GPS and accelerometer, which measure how the vehicle is being driven, for example smooth versus sudden acceleration.

All of this data is bundled and sent to central servers, where a machine learning algorithm builds a profile of the driver from their driving pattern. Consider two profiles:

1. Engine rpm always below 3000, vehicle speed never above 80 kmph, and very smooth accelerometer data.
2. Engine rpm going up to 7000, vehicle speed frequently above 140 kmph, and sudden, abrupt acceleration.

We could classify the first driver as safe and the second as risky, with many more profiles in between. Under UBI, the algorithm classifies drivers by their driving pattern and gives each a score out of 100. Insurance companies then give discounts to drivers deemed safe, as they are less prone to accidents.

Where IoT goes from here

This is one of the biggest applications of IoT in the automotive sector right now, but it is by no means the only one. We have only started to scratch the surface. Many other applications, including diagnostics, prognostics, real-time demand calculation (for EVs) and scheduling, become possible once IoT is widely used in all automobiles.